1 Todo
2 Load some libraries
3 Import the data
4 Overview
5 Export csv file (gzipped)
6 PM10
7 O3
8 Number of missing observations by stazione, year and month (PM10)
9 Highcharter
10 Vegalite
11 Dygraph
12 Dygraph, trimmed values
13 rbokeh
14 Maximum per year (PM10)
15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)
16 Mappa stazioni

1 Todo

2 Load some libraries

library('readr')
library('stringr')
library('tidyr')
library('ggplot2')
library('dplyr')
library('highcharter')
library('vegalite')
library('lubridate')
library('leaflet')
library('dygraphs')
library('xts')
library('rbokeh')

ma_theme <- function(base_size=12, base_family="helvetica") {
                      theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
                      legend.position = 'bottom')
}

split_names <- function(m) {

   stazione <- zz$stazione[which(str_detect(m, zz$stazione))]
   inq      <- zz$inq[which(str_detect(m, zz$inq))]
   tdm      <- zz$tdm[which(str_detect(m, zz$tdm))]
   out <- paste(stazione, inq, tdm, sep = "__")
   out
}

# tdm -> tipo di misura

zz <- read.table(header = TRUE, sep = ";", text = "
stazione;inq;tdm
partinico;SO2;Maxorario
sasol;CO;Media24h
enna;NO2;Maxmedia8h
ex_autoparco;O3;NA
trapani;Benzene;NA
termini;PM 10;NA
cda_gabbia;PM 2e5;NA
termica_milazzo;NA;NA
boccetta;NA;NA
Marcellino;NA;NA
misterbianco;NA;NA
megara;NA;NA
lab_mobile1;NA;NA
PORTO EMPEDOCLE;NA;NA
VILLA AUGUSTA;NA;NA
parcheggio_agip;NA;NA
CAPACI;NA;NA
ISOLA DELLE FEMMINE;NA;NA
")

zz %>% knitr::kable()

stazione	inq	tdm
partinico	SO2	Maxorario
sasol	CO	Media24h
enna	NO2	Maxmedia8h
ex_autoparco	O3	NA
trapani	Benzene	NA
termini	PM 10	NA
cda_gabbia	PM 2e5	NA
termica_milazzo	NA	NA
boccetta	NA	NA
Marcellino	NA	NA
misterbianco	NA	NA
megara	NA	NA
lab_mobile1	NA	NA
PORTO EMPEDOCLE	NA	NA
VILLA AUGUSTA	NA	NA
parcheggio_agip	NA	NA
CAPACI	NA	NA
ISOLA DELLE FEMMINE	NA	NA

3 Import the data

# http://www.arpa.sicilia.it/storage/MAria_report.xls
x <- read_csv("http://blog.spaziogis.it/static/ods/data/mariareport/MAria_report_02.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   data = col_date(format = ""),
##   CAPACI_SO2_Maxorario = col_character(),
##   CAPACI_SO2_Media24h = col_character(),
##   CAPACI_CO_Maxorario = col_character(),
##   CAPACI_CO_Maxmedia8h = col_character(),
##   `CAPACI_PM 10_Media24h` = col_character(),
##   `CAPACI_PM 2e5_Media24h` = col_character(),
##   `ISOLA DELLE FEMMINE_SO2_Maxorario` = col_character(),
##   `ISOLA DELLE FEMMINE SO2_Media24h` = col_character(),
##   `ISOLA DELLE FEMMINE CO_Maxorario` = col_character(),
##   `ISOLA DELLE FEMMINE CO_Maxmedia8h` = col_character(),
##   `ISOLA DELLE FEMMINE NO2_Maxorario` = col_character(),
##   `ISOLA DELLE FEMMINE PM 10_Media24h` = col_character(),
##   `ISOLA DELLE FEMMINE PM 2e5_Media24h` = col_character()
## )

## See spec(...) for full column specifications.

colnames(x) <- c("date", unlist(lapply(colnames(x), split_names)))

xm <- gather(x, variable, value, -date)

u <- data.frame(do.call("rbind", strsplit(xm$variable, "__")), stringsAsFactors = FALSE)
colnames(u) <- c("stazione", "inq", "tdm")
fin <- data.frame(u, date = xm$date, value = as.numeric(xm$value))
fin$inq <- gsub("PM 2e5", "PM 2.5", fin$inq)

knitr::kable(head(fin))

stazione	inq	tdm	date	value
partinico	SO2	Maxorario	2013-01-01	2.227614
partinico	SO2	Maxorario	2013-01-02	1.331977
partinico	SO2	Maxorario	2013-01-03	4.150291
partinico	SO2	Maxorario	2013-01-04	7.960566
partinico	SO2	Maxorario	2013-01-05	3.602461
partinico	SO2	Maxorario	2013-01-06	1.674221

knitr::kable(tail(fin))

	stazione	inq	tdm	date	value
123887	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-05	13.4
123888	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-06	9.1
123889	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-07	7.7
123890	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-08	9.0
123891	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-09	9.4
123892	ISOLA DELLE FEMMINE	PM 2.5	Media24h	2016-08-10	NA

# most recent day
max(as.Date(fin[!is.na(fin$value), "date"]))

## [1] "2016-08-09"

addmargins(table(fin$inq, fin$tdm, useNA = "always")) %>% knitr::kable()

	Maxmedia8h	Maxorario	Media24h	Sum
Benzene	0	18452	0	18452
CO	11862	11862	0	23724
NO2	0	14498	0	14498
O3	11862	11862	0	23724
PM 10	0	0	13180	13180
PM 2.5	0	0	3954	3954
SO2	0	13180	13180	26360
NA	0	0	0	0
Sum	23724	69854	30314	123892

Read the data

4 Overview

addmargins(table(fin$stazione, !is.na(fin$value), useNA= "always")) %>% knitr::kable()

	FALSE	TRUE	Sum
boccetta	7176	732	7908
CAPACI	12486	694	13180
cda_gabbia	629	4643	5272
enna	1214	10648	11862
ex_autoparco	323	995	1318
ISOLA DELLE FEMMINE	8784	442	9226
Marcellino	520	798	1318
megara	257	1061	1318
misterbianco	762	11100	11862
parcheggio_agip	306	1012	1318
partinico	540	11322	11862
PORTO EMPEDOCLE	12239	941	13180
termica_milazzo	1313	7913	9226
termini	913	10949	11862
trapani	1221	10641	11862
VILLA AUGUSTA	1015	303	1318
NA	0	0	0
Sum	49698	74194	123892

addmargins(with(fin[!is.na(fin$value), ], table(stazione, inq, useNA= "always"))) %>% knitr::kable()

	Benzene	CO	NO2	O3	PM 10	PM 2.5	SO2	Sum
boccetta	0	0	138	278	40	0	276	732
CAPACI	60	137	146	99	60	62	130	694
cda_gabbia	1093	0	1188	0	0	0	2362	4643
enna	971	2541	1265	2484	1225	0	2162	10648
ex_autoparco	995	0	0	0	0	0	0	995
ISOLA DELLE FEMMINE	0	134	70	0	59	51	128	442
Marcellino	798	0	0	0	0	0	0	798
megara	1061	0	0	0	0	0	0	1061
misterbianco	1118	2563	1176	2495	1276	0	2472	11100
parcheggio_agip	1012	0	0	0	0	0	0	1012
partinico	1115	2594	1304	2493	1260	0	2556	11322
PORTO EMPEDOCLE	86	127	112	222	87	83	224	941
termica_milazzo	1128	1836	1292	2417	1240	0	0	7913
termini	1050	2541	1283	2538	1211	0	2326	10949
trapani	1143	2341	1229	2414	1154	0	2360	10641
VILLA AUGUSTA	303	0	0	0	0	0	0	303
NA	0	0	0	0	0	0	0	0
Sum	11933	14814	9203	15440	7612	196	14996	74194

5 Export csv file (gzipped)

con_out <- gzfile("maria_report_04_long.csv.gz", open = "wb")
   write.csv(fin, file = con_out, row.names = FALSE)
close(con_out)

6 PM10

fin_pm10 <- subset(fin, subset = stazione %in% c("enna", "trapani", "partinico", 
                                                 "misterbianco", "termini", 
                                                 "termica_milazzo") & 
                        inq == "PM 10" & tdm == "Media24h")
p1 <- ggplot(fin_pm10, aes(x=as.Date(date), y=value, col= stazione)) + geom_line()
p1 <- p1 + geom_smooth()
p1 <- p1 + facet_wrap( ~ stazione, nrow = 1)
p1 <- p1 + geom_hline(yintercept = 50, col = "blue")
p1 <- p1 + labs(x = "Date", y = "value")
p1 <- p1 + ma_theme()
p1

## Warning: Removed 542 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_path).

7 O3

fin_o3 <- subset(fin, subset = stazione %in% c("enna", "trapani", "partinico", 
                                               "misterbianco", "termini", 
                                               "termica_milazzo") & 
                        inq == "O3" & tdm == "Maxmedia8h")
p1 <- ggplot(fin_o3, aes(x=as.Date(date), y=value, col= stazione)) + geom_line()
p1 <- p1 + geom_smooth()
p1 <- p1 + facet_wrap( ~ stazione, nrow = 1)
p1 <- p1 + labs(x = "Date", y = "value")
p1 <- p1 + ma_theme()
p1

## Warning: Removed 490 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_path).

8 Number of missing observations by stazione, year and month (PM10)

missing_by_yr_month <- function(x) {
  z <- tbl_df(x) %>%
       dplyr::filter(is.na(value)) %>%
       mutate(yr = year(ymd(date)), month = month(ymd(date))) %>%
       select(stazione, yr, month) %>% 
       ungroup()
  with(z, table(yr, month, stazione))
}

(kx1 <- missing_by_yr_month(fin_pm10))

## , , stazione = enna
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  2  0  0  0  0  0  0  0  0  0  0  1
##   2014  0  0  3  0  0  0  1  2  3  6  0  4
##   2015  1  2  4 23  0  2  2  0  1  3  2  5
##   2016  6  4  0  4  4  7  0  1  0  0  0  0
## 
## , , stazione = misterbianco
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  1  0  1  0  0  0  0  0  0  1  1
##   2014  7  0  6  0  0  0  0  0  1  3  0  0
##   2015  0  0  1  1  0  0  3  2  0  2  1  5
##   2016  0  0  0  1  2  0  2  1  0  0  0  0
## 
## , , stazione = partinico
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  3  0  0  0  0  0  0  0  2  9  4  1
##   2014  8  1  0  0  1  0  1  0  1  0  0  0
##   2015  0  1  1  1  0  0  1  6  4  2  2  2
##   2016  2  0  1  0  2  0  0  2  0  0  0  0
## 
## , , stazione = termica_milazzo
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  1  0  0  0  0  0  3  0  0  0  1
##   2014  0  0  0  0  0  0  0  0  0  0  0  0
##   2015  0  0  0  0  0  2  0 10  0  0 17 11
##   2016  7  6  5  0  1  0  5  9  0  0  0  0
## 
## , , stazione = termini
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  2  0  0  0  0  0  0  0  0  1  4
##   2014  0  0  7  0  0  0  0  0  0  5 11 31
##   2015 21  0  1  0  0  1  1  2  2  0  4  1
##   2016  0  1  0  6  2  0  2  2  0  0  0  0
## 
## , , stazione = trapani
## 
##       month
## yr      1  2  3  4  5  6  7  8  9 10 11 12
##   2013  0  0  8 14  0  0  7 12  0  0  1  0
##   2014  0  0  3 30  5  1 13 16  7  3  0  0
##   2015 10  4  1  4  4  1  3  0  1  3  1  2
##   2016  0  0  1  3  4  0  0  2  0  0  0  0

# year 2016
addmargins(t(kx1["2016",1:12,]), 1)

##                  month
## stazione           1  2  3  4  5  6  7  8  9 10 11 12
##   enna             6  4  0  4  4  7  0  1  0  0  0  0
##   misterbianco     0  0  0  1  2  0  2  1  0  0  0  0
##   partinico        2  0  1  0  2  0  0  2  0  0  0  0
##   termica_milazzo  7  6  5  0  1  0  5  9  0  0  0  0
##   termini          0  1  0  6  2  0  2  2  0  0  0  0
##   trapani          0  0  1  3  4  0  0  2  0  0  0  0
##   Sum             15 11  7 14 15  7  9 17  0  0  0  0

# Trim observations from each end before the computation
trim_obs <- function(x, trim) {
  m <- c(quantile(x, probs = trim, na.rm = TRUE), 
         quantile(x, probs = 1 - trim, na.rm = TRUE))
  print(m)
  out <- (x >= m[1] & x <= m[2])
  return(out)
}

fin_pm10_trim <- tbl_df(fin_pm10) %>%
                 group_by(stazione) %>%
                 mutate(ixd = trim_obs(value, 0.5/100)) %>%
                 ungroup()

##     0.5%    99.5% 
##  0.15648 84.65535 
##    0.5%   99.5% 
##  5.0825 90.7675 
##     0.5%    99.5% 
##  5.48850 99.98974 
##     0.5%    99.5% 
##  7.49696 84.98849 
##    0.5%   99.5% 
##  1.2196 74.0582 
##     0.5%    99.5% 
##  6.07956 89.34437

table(fin_pm10_trim$stazione, fin_pm10_trim$ixd)

##                  
##                   FALSE TRUE
##   enna               14 1211
##   misterbianco       14 1262
##   partinico          14 1246
##   termica_milazzo    14 1226
##   termini            14 1197
##   trapani            12 1142

fin_pm10_trim <- dplyr::filter(fin_pm10_trim, ixd == TRUE)

p2 <- ggplot(fin_pm10_trim, aes(x=as.Date(date), y=value, col = stazione))
p2 <- p2 + geom_smooth()
p2 <- p2 + facet_wrap( ~ stazione, nrow = 1)
p2 <- p2 + labs(x = "Date", y = "value")
p2 <- p2 + ma_theme()
p2

fin_pm10_trim$date <- as.character(year(as.Date(fin_pm10_trim$date)))

p3 <- ggplot(fin_pm10_trim, aes(x = date, y = value, group = stazione, col = stazione))
p3 <- p3 + geom_boxplot(group = 2) + geom_jitter(width = 0.2, alpha = 0.35)
p3 <- p3 + facet_wrap( ~ stazione, nrow = 1)
p3 <- p3 + labs(x = "Date", y = "value")
p3 <- p3 + ma_theme()
p3

9 Highcharter

fin_hc <- tbl_df(fin) %>% 
          dplyr::filter(stazione %in% c("enna", "trapani", "termica_milazzo", 
                                        "partinico", "termini"), 
                        inq == "PM 10", tdm == "Media24h") %>%
          mutate(year_month = format(as.Date(date), "%Y-%m")) %>%
          group_by(stazione, year_month) %>%
          summarise(mean_val = round(mean(value, na.rm = TRUE), 2)) %>%
          ungroup()

fin_hc <- data.frame(fin_hc)

hc1 <- highchart() %>% 
        hc_xAxis(categories = fin_hc$year_month) %>% 
        hc_add_series(name = "Enna", data = fin_hc[fin_hc$stazione=="enna", "mean_val"]) %>%
        hc_add_series(name = "Partinico", data = fin_hc[fin_hc$stazione=="partinico", "mean_val"]) %>%
        hc_add_series(name = "Termica Milazzo", data = fin_hc[fin_hc$stazione=="termica_milazzo", "mean_val"]) %>%
        hc_add_series(name = "Termini", data = fin_hc[fin_hc$stazione=="termini", "mean_val"]) %>%
        hc_add_series(name = "Trapani", data = fin_hc[fin_hc$stazione=="trapani", "mean_val"])

hc1

10 Vegalite

vegalite(export=TRUE) %>%
  cell_size(400, 400) %>% 
  add_data(fin_hc) %>%
  encode_x("year_month", "temporal") %>%
  encode_y("mean_val", "quantitative") %>%
  encode_color("stazione", "nominal") %>% 
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  mark_line() -> vl

vl

11 Dygraph

xdy <- fin_pm10[fin_pm10$stazione == "enna", ]
dygraph(xts(xdy$value, order.by = as.POSIXct(xdy$date)), 
            main = "Enna // Inquinante: PM10")  %>% 
        dyRangeSelector()

12 Dygraph, trimmed values

fin_pm10_trim <- tbl_df(fin_pm10) %>%
                 group_by(stazione) %>%
                 mutate(ixd = trim_obs(value, 0.5/100)) %>%
                 ungroup()

##     0.5%    99.5% 
##  0.15648 84.65535 
##    0.5%   99.5% 
##  5.0825 90.7675 
##     0.5%    99.5% 
##  5.48850 99.98974 
##     0.5%    99.5% 
##  7.49696 84.98849 
##    0.5%   99.5% 
##  1.2196 74.0582 
##     0.5%    99.5% 
##  6.07956 89.34437

fin_pm10_trim <- dplyr::filter(fin_pm10_trim, ixd == TRUE)

xdy1 <- fin_pm10_trim[fin_pm10_trim$stazione == "enna", ]
dygraph(xts(xdy1$value, order.by = as.POSIXct(xdy1$date)), 
            main = "Enna // Inquinante: PM10, trimmed values")  %>% 
        dyRangeSelector()

13 rbokeh

# Trimmed values, PM10, Enna

figure(width = 800, height = 600) %>%
       ly_hist(value, data = xdy1, breaks = 40, freq = FALSE) %>%
       ly_density(value, data = xdy1)

xdy2 <- fin_pm10_trim[fin_pm10_trim$stazione %in% c("enna", "termini"), ]

figure(legend_location = "top_left") %>%
  ly_quantile(value, group = stazione, data = xdy2)

14 Maximum per year (PM10)

tbl_df(fin) %>% 
          mutate(year = format(as.Date(date), "%Y")) %>%
          dplyr::filter(inq == "PM 10", tdm == "Media24h") %>%
          group_by(stazione, year) %>%
          summarise(max_val = round(max(value, na.rm = TRUE), 2)) %>%
          ungroup() %>%
          spread(year, max_val) %>% knitr::kable()

stazione	2013	2014	2015	2016
boccetta	28.22	37.60	NA	NA
CAPACI	NA	NA	NA	74.80
enna	81.86	91.95	101.20	216.60
ISOLA DELLE FEMMINE	NA	NA	NA	76.90
misterbianco	74.32	137.28	112.88	306.40
partinico	64.08	462.20	195.80	229.72
PORTO EMPEDOCLE	NA	NA	75.10	49.90
termica_milazzo	99.03	103.10	89.70	156.00
termini	62.62	135.90	70.30	292.70
trapani	61.04	183.87	74.50	141.60

15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)

m <- tbl_df(fin) %>%
       dplyr::filter(inq == "PM 10") %>%
       mutate(year = format(as.Date(date), "%Y")) %>%
       group_by(stazione, year) %>%
       mutate(days_gt50 = cumsum(ifelse(!is.na(value) & value > 50, 1, 0))) %>%
       select(stazione, year, days_gt50) %>%
       top_n(1, days_gt50)
       #distinct(stazione, year)

spread(unique(m), year, days_gt50) %>% knitr::kable()

stazione	2013	2014	2015	2016
boccetta	0	0	0	0
CAPACI	0	0	0	1
enna	4	9	6	5
ISOLA DELLE FEMMINE	0	0	0	1
misterbianco	4	16	5	4
partinico	4	21	9	6
PORTO EMPEDOCLE	0	0	29	0
termica_milazzo	5	18	8	8
termini	2	9	6	6
trapani	2	12	1	4

16 Mappa stazioni

geo_stazioni <- read.csv("http://www.covimo.de/arpa/stazioni.csv")
leaflet(data = geo_stazioni[1:11, ]) %>% 
        addTiles() %>%
        addMarkers(~longitude, ~latitude, popup = ~as.character(Cartella))

devtools::session_info()

## Session info --------------------------------------------------------------

##  setting  value                                      
##  version  R version 3.3.1 Patched (2016-06-24 r70831)
##  system   x86_64, mingw32                            
##  ui       RTerm                                      
##  language (EN)                                       
##  collate  German_Germany.1252                        
##  tz       Europe/Berlin                              
##  date     2016-08-12

## Packages ------------------------------------------------------------------

##  package     * version    date       source                            
##  assertthat    0.1        2013-12-06 CRAN (R 3.3.1)                    
##  base64        2.0        2016-05-10 CRAN (R 3.3.1)                    
##  broom         0.4.1      2016-06-24 CRAN (R 3.3.1)                    
##  chron         2.3-47     2015-06-24 CRAN (R 3.3.1)                    
##  clipr         0.2.1      2016-06-23 CRAN (R 3.3.1)                    
##  codetools     0.2-14     2015-07-15 CRAN (R 3.3.1)                    
##  colorspace    1.2-6      2015-03-11 CRAN (R 3.3.1)                    
##  curl          1.1        2016-07-26 CRAN (R 3.3.1)                    
##  data.table    1.9.6      2015-09-19 CRAN (R 3.3.1)                    
##  DBI           0.4-1      2016-05-08 CRAN (R 3.3.1)                    
##  devtools      1.12.0     2016-06-24 CRAN (R 3.3.1)                    
##  digest        0.6.10     2016-08-02 CRAN (R 3.3.1)                    
##  dplyr       * 0.5.0      2016-06-24 CRAN (R 3.3.1)                    
##  dygraphs    * 1.1.1.0    2016-08-01 CRAN (R 3.3.1)                    
##  evaluate      0.9        2016-04-29 CRAN (R 3.3.1)                    
##  formatR       1.4        2016-05-09 CRAN (R 3.3.1)                    
##  ggplot2     * 2.1.0      2016-03-01 CRAN (R 3.3.1)                    
##  gistr         0.3.6      2015-12-22 CRAN (R 3.3.1)                    
##  gtable        0.2.0      2016-02-26 CRAN (R 3.3.1)                    
##  hexbin        1.27.1     2015-08-19 CRAN (R 3.3.1)                    
##  highcharter * 0.4.0      2016-07-15 CRAN (R 3.3.1)                    
##  highr         0.6        2016-05-09 CRAN (R 3.3.1)                    
##  htmltools     0.3.5      2016-03-21 CRAN (R 3.3.1)                    
##  htmlwidgets   0.7        2016-08-02 CRAN (R 3.3.1)                    
##  httr          1.2.1      2016-07-03 CRAN (R 3.3.1)                    
##  igraph        1.0.1      2015-06-26 CRAN (R 3.3.0)                    
##  jsonlite      1.0        2016-07-01 CRAN (R 3.3.1)                    
##  knitr         1.13       2016-05-09 CRAN (R 3.3.1)                    
##  labeling      0.3        2014-08-23 CRAN (R 3.3.0)                    
##  lattice       0.20-33    2015-07-14 CRAN (R 3.3.1)                    
##  lazyeval      0.2.0      2016-06-12 CRAN (R 3.3.1)                    
##  leaflet     * 1.0.1.9004 2016-06-26 Github (rstudio/leaflet@e2751ff)  
##  lubridate   * 1.5.6      2016-04-06 CRAN (R 3.3.1)                    
##  magrittr      1.5        2014-11-22 CRAN (R 3.3.1)                    
##  maps          3.1.1      2016-07-27 CRAN (R 3.3.1)                    
##  Matrix        1.2-6      2016-05-02 CRAN (R 3.3.1)                    
##  memoise       1.0.0      2016-01-29 CRAN (R 3.3.1)                    
##  mgcv          1.8-13     2016-07-21 CRAN (R 3.3.1)                    
##  mnormt        1.5-4      2016-03-09 CRAN (R 3.3.0)                    
##  munsell       0.4.3      2016-02-13 CRAN (R 3.3.1)                    
##  nlme          3.1-128    2016-05-10 CRAN (R 3.3.1)                    
##  openssl       0.9.4      2016-05-25 CRAN (R 3.3.1)                    
##  plyr          1.8.4      2016-06-08 CRAN (R 3.3.1)                    
##  pryr          0.1.2      2015-06-20 CRAN (R 3.3.1)                    
##  psych         1.6.6      2016-06-28 CRAN (R 3.3.1)                    
##  purrr         0.2.2      2016-06-18 CRAN (R 3.3.1)                    
##  quantmod      0.4-5      2015-07-24 CRAN (R 3.3.1)                    
##  R6            2.1.2      2016-01-26 CRAN (R 3.3.1)                    
##  rbokeh      * 0.4.2      2016-07-09 Github (bokeh/rbokeh@1a73a42)     
##  Rcpp          0.12.6     2016-07-19 CRAN (R 3.3.1)                    
##  readr       * 1.0.0.9000 2016-08-05 Github (hadley/readr@a714a8e)     
##  reshape2      1.4.1      2014-12-06 CRAN (R 3.3.1)                    
##  rlist         0.4.6.1    2016-04-04 CRAN (R 3.3.1)                    
##  rmarkdown     1.0.9002   2016-08-06 Github (rstudio/rmarkdown@4f66e62)
##  scales        0.4.0      2016-02-26 CRAN (R 3.3.1)                    
##  stringi       1.1.1      2016-05-27 CRAN (R 3.3.0)                    
##  stringr     * 1.0.0      2015-04-30 CRAN (R 3.3.1)                    
##  tibble        1.1        2016-07-04 CRAN (R 3.3.1)                    
##  tidyr       * 0.5.1      2016-06-14 CRAN (R 3.3.1)                    
##  TTR           0.23-1     2016-03-21 CRAN (R 3.3.1)                    
##  vegalite    * 0.6.1      2016-06-26 Github (hrbrmstr/vegalite@8c31c1b)
##  viridisLite   0.1.3      2016-03-12 CRAN (R 3.3.1)                    
##  webshot       0.3.2      2016-06-23 CRAN (R 3.3.1)                    
##  withr         1.0.2      2016-06-20 CRAN (R 3.3.1)                    
##  xts         * 0.9-7      2014-01-02 CRAN (R 3.3.1)                    
##  yaml          2.1.13     2014-06-12 CRAN (R 3.3.1)                    
##  zoo         * 1.7-13     2016-05-03 CRAN (R 3.3.1)

Maria_report

Patrick Hausmann

1 Todo

2 Load some libraries

3 Import the data

4 Overview

5 Export csv file (gzipped)

6 PM10

7 O3

8 Number of missing observations by stazione, year and month (PM10)

9 Highcharter

10 Vegalite

11 Dygraph

12 Dygraph, trimmed values

13 rbokeh

14 Maximum per year (PM10)

15 Days per year with a PM10 value over 50 mg/m3 (max. 35 excedances allowed per year)

16 Mappa stazioni